Nature-inspired computing : physics- and chemistry-based algorithms

Provides a comprehensive introduction to physics and chemistry-based algorithms in nature-inspired computingCovers many step-by-step algorithmsExplores applications to real-life engineering problems Cover -- Half Title -- Title Page -- Copyright page -- Contents -- Foreword -- Preface -- Acknowledgments -- Authors -- Chapter 1: Dialectics of Nature: Inspiration for Computing -- 1.1 Inspiration from Nature -- 1.2 Brief History of Natural Sciences -- 1.2.1 Laws of Motion -- 1.2.2 Law of Gravitation -- 1.2.3 Transformation between Heat and Mechanical Energy -- 1.2.4 Transformation between Mass and Energy -- 1.2.5 Light and Optics -- 1.2.6 Sound and Acoustics -- 1.2.7 Hydrology and Dynamics -- 1.2.8 Development in Chemistry -- 1.2.9 Development in Biological Sciences -- 1.3 Traditional Approaches to Search and Optimization -- 1.3.1 Line Search -- 1.3.2 Golden Section Search -- 1.3.3 Fibonacci Search -- 1.3.4 Newton's Method -- 1.3.5 Secant Method -- 1.3.6 Gradient-Based Methods -- 1.3.6.1 Descent Methods -- 1.3.6.2 Gradient Methods -- 1.3.6.3 Steepest Descent Method (or Gradient Descent) -- 1.3.7 Classical Newton's Method -- 1.3.8 Modified Newton's Method -- 1.3.9 Levenberg-Marquardt Modification -- 1.3.10 Quasi-Newton Method -- 1.3.11 Conjugate Direction Methods -- 1.3.12 Conjugate Gradient Methods -- 1.3.13 BFGS Method -- 1.3.14 Deterministic vs Stochastic Algorithms -- 1.3.15 Local Search Methods -- 1.3.15.1 Scatter Search -- 1.3.15.2 Tabu Search (TS) -- 1.3.15.3 Random Search (RS) -- 1.3.15.4 Downhill Simplex (Nelder-Mead) Method -- 1.4 Paradigm of NIC -- 1.5 Physics-Based Algorithms -- 1.6 Chemistry-Based Algorithms -- 1.7 Biology-Based Algorithms -- 1.8 Culture-, Society-, and Civilization-Based Algorithms -- 1.9 Overview -- 1.10 Conclusion -- References -- Chapter 2: Gravitational Search Algorithm -- 2.1 Introduction -- 2.2 Physics of Gravity -- 2.2.1 Acceleration of Objects -- 2.2.2 Mass of Moving Objects -- 2.3 Gravitational Search Algorithm -- 2.4 Parameters of GSA -- 2.5 Fitness Function Content: Swarm Intelligence-Based Algorithms Bio-Inspired Algorithms Physics and Chemistry-Based Algorithms